Enhancing Scientific Discourse: Machine Translation for the Scientific Domain
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Abstract (arXiv)
The increasing volume of scientific research necessitates effective communication across language barriers. Machine translation (MT) offers a promising solution for accessing international publications. However, the scientific domain presents unique challenges due to its specialized vocabulary and complex sentence structures. In this paper, we present the development of a collection of parallel and monolingual corpora for the scientific domain. The corpora target the language pairs Spanish-English, French-English, and Portuguese-English. For each language pair, we create a large general scientific corpus as well as four smaller corpora focused on the domains of: Cancer Research, Energy Research, Neuroscience, and Transportation research. To evaluate the quality of these corpora, we utilize them for fine-tuning general-purpose neural machine translation (NMT) systems. We provide details regarding the corpus creation process, the fine-tuning strategies employed, and we conclude with the evaluation results.
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References · 11
- 2017Attention Is All You Needcited by 60,361
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- 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
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- 2020The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTcited by 128
- 2018Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddingscited by 83
- 2019A Large Parallel Corpus of Full-Text Scientific Articlescited by 12
- 2019A Parallel Corpus of Theses and Dissertations Abstractscited by 3
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